问题描述

现在我有一个 4 阶段的 MapReduce 作业，如下所示:

Now I have a 4-phase MapReduce job as follows:

Input-> Map1 -> Reduce1 -> Reducer2 -> Reduce3 -> Reduce4 -> Output

我注意到 Hadoop 中有一个 ChainMapper 类，它可以将多个映射器链接成一个大映射器，并节省映射阶段之间的磁盘 I/O 成本.还有一个 ChainReducer 类，但它不是真正的Chain-Reducer".它只能支持以下工作:

I notice that there is ChainMapper class in Hadoop which can chain several mappers into one big mapper, and save the disk I/O cost between map phases. There is also a ChainReducer class, however it is not a real "Chain-Reducer". It can only support jobs like:

[Map+/ Reduce Map*]

我知道我可以为我的任务设置四个 MR 作业，并为最后三个作业使用默认映射器.但这将花费大量磁盘 I/O，因为 reducer 应该将结果写入磁盘以让以下映射器访问它.是否有任何其他 Hadoop 内置功能可以链接我的 reducer 以降低 I/O 成本?

I know I can set four MR jobs for my task, and use default mappers for the last three jobs. But that will cost a lot of disk I/O, since reducers should write the result into disk to let the following mapper access it. Is there any other Hadoop built-in feature to chain my reducers to lower the I/O cost?

我使用的是 Hadoop 1.0.4.

I am using Hadoop 1.0.4.

推荐答案

我不认为你可以将一个reducer的o/p直接交给另一个reducer.我会为此而努力的:

I dont think that you can have the o/p of a reducer being given to another reducer directly. I would have gone for this:

Input-> Map1 -> Reduce1 -> 
        Identity mapper -> Reducer2 -> 
                Identity mapper -> Reduce3 -> 
                         Identity mapper -> Reduce4 -> Output

在 Hadoop 2.X 系列中，在内部，您可以使用 ChainMapper 在 reducer 之前链接 mapper，在 reducer 之后使用 ChainReducer.

In Hadoop 2.X series, internally you can chain mappers before reducer with ChainMapper and chain Mappers after reducer with ChainReducer.

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